A Machine Learning Model for Identifying Sexual HealthInfluencers to Promote the Secondary Distribution of HIVSelf-Testing Among Gay, Bisexual, and Other Men Who HaveSex With Men in China:Quasi-Experimental Study

Background: Sexual health influencers (SHIs) are individuals actively sharing sexual health information with their peers, andthey play an important role in promoting HIV care services, including the secondary distribution of HIV self-testing (SD-HIVST).Previous studies used a 6-item empirical leadership scale to identify SHIs. However, this approach may be biased as it does notconsider individuals'social networks. Objective: This study used a quasi-experimental study design to evaluate how well a newly developed machine learning (ML)model identifies SHIs in promoting SD-HIVST compared to SHIs identified by a scale whose validity had been tested before Methods: We recruited participants from BlueD, the largest social networking app for gay men in China. Based on their responsesto the baseline survey, the ML model and scale were used to identify SHIs, respectively. This study consisted of 2 rounds, differingin the upper limit of the number of HIVST kits and peer-referral links that SHIs could order and distribute (first round <= 5 andsecond round <= 10). Consented SHIs could order multiple HIV self-testing (HIVST) kits and generate personalized peer-referrallinks through a web-based platform managed by a partnered gay-friendly community-based organization. SHIs were encouragedto share additional kits and peer-referral links with their social contacts (defined as "alters"). SHIs would receive US $3 incentiveswhen their corresponding alters uploaded valid photographic testing results to the same platform. Our primary outcomes included(1) the number of alters who conducted HIVST in each group and (2) the number of newly tested alters who conducted HIVSTin each. We used negative binomial regression to examine group differences during the first round (February-June 2021), thesecond round (June-November 2021), and the combined first and second rounds, respectively. Results: In January 2021, a total of 1828 men who have sex with men (MSM) completed the survey. Overall, 393 SHIs (scale=195and ML model=198) agreed to participate in SD-HIVST. Among them, 229 SHIs (scale=116 and ML model=113) ordered HIVSTon the web. Compared with the scale group, SHIs in the ML model group motivated more alters to conduct HIVST (meandifference [MD] 0.88, 95% CI 0.02-2.22; adjusted incidence risk ratio [aIRR] 1.77, 95% CI 1.07-2.95) when we combined thefirst and second rounds. Although the mean number of newly tested alters was slightly higher in the ML model group than in thescale group, the group difference was insignificant (MD 0.35, 95% CI -0.17 to -0.99; aIRR 1.49, 95% CI 0.74-3.02). Conclusions: Among Chinese MSM, SHIs identified by the ML model can motivate more individuals to conduct HIVST thanthose identified by the scale. Future research can focus on how to adapt the ML model to encourage newly tested individuals toconduct HIVST.Trial Registration: Chinese Clinical Trials Registry ChiCTR2000039632;https://www.chictr.org.cn/showprojEN.html?proj=63068